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Record W2782866552 · doi:10.1109/icecta.2017.8251997

DCT- and FFT-based OFDM systems with continuous phase modulation over flat fading channels

2017· article· en· W2782866552 on OpenAlexaff
Rayan Hamza Alsisi, Raveendra K. Rao

Bibliographic record

Venue2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA) · 2017
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsWestern University
FundersIslamic University of Madinah
KeywordsOrthogonal frequency-division multiplexingFadingAlgorithmBit error rateModulation (music)Rayleigh fadingComputer scienceFast Fourier transformDiscrete Fourier transform (general)Electronic engineeringDiscrete cosine transformMathematicsFourier transformFractional Fourier transformTelecommunicationsChannel (broadcasting)PhysicsEngineeringMathematical analysisAcousticsFourier analysisArtificial intelligence

Abstract

fetched live from OpenAlex

Constant envelope Orthogonal Frequency Division Multiplexing (OFDM) system is considered and examined for Bit Error Rate (BER) performance over slowly varying Rayleigh and Rician fading channels. In the system, fast Fourier and discrete cosine transform techniques are used in conjunction with Continuous Phase Modulation (CPM). Analytical expressions for BER that are easy for numerical computation have been derived and illustrated as function of Eb/N0Signal-to-Noise Ratio (SNR), h, the modulation index, M, number of amplitude levels in the data mapper, and statistical parameters of fading distributions. It is observed that OFDM system with Discrete Cosine Transform (DCT) is superior in many ways including BER performance compared to the corresponding system with Fast Fourier Transform (FFT).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.285
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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